Generative Engine Optimization

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Generative Engine Optimization (GEO) examines how changes to web content affect what AI search and recommendation systems retrieve, cite, and say about a source. Common approaches include controlled edits to content structure and query alignment, retrieval-augmented generation tests, and agentic systems that iteratively rewrite pages using engine feedback. Researchers also study how to measure visibility across prompts and repeated runs, and whether changes actually cause more traffic or citations. A central tension is that optimization can improve discoverability but also manipulate recommendations or weaken content quality, while results vary with retrieval, brand authority, and the engine being tested.

Latest papers

40 papers7 papers in the last four weeks, up 17% on the four weeks before. 0.0% of all new papers.

How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews
Apr 30, 2026Riley Grossman, Songjiang Liu, Michael K. Chen +3Generative Engine Optimization Generative Retrieval
New Jersey Institute of Technology Newark, NJ, USA · Nanyang Technological University Singapore, Singapore · Indiana University Bloomington Bloomington, IN, USA
From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning
Apr 21, 2026Beining Wu, Fuyou Mao, Jiong Lin +7Generative Engine Optimization LLM Agent Skill Learning
Hangzhou Dianzi University · Central South University · Shanghai Artificial Intelligence Laboratory +3
Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility
Apr 21, 2026Zikang Liu, Peilan XuGenerative Engine Optimization Multi-Objective Optimization
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Don't Measure Once: Measuring Visibility in AI Search (GEO)
Apr 8, 2026Julius Schulte, Malte Bleeker, Philipp KaufmannGenerative Engine Optimization Information Retrieval
Global Center for Entrepreneurship and Innovation University of St. Gallen 9000 St.Gallen, Switzerland · Marketing und Customer Insight University of St. Gallen 9000 St.Gallen, Switzerland
Beyond Retrieval: Modeling Confidence Decay and Deterministic Agentic Platforms in Generative Engine Optimization
Apr 4, 2026XinYu Zhao, ChengYou Li, XiangBao Meng +2Generative Engine Optimization LLM Agent Orchestration
Yishu Research · Fukuoka Institute of Technology
Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior
Mar 31, 2026Junwei Yu, Mufeng Yang, Yepeng Ding +1Generative Engine Optimization
The University of Tokyo, Tokyo, Japan · University of Tsukuba, Ibaraki, Japan · Hiroshima University, Hiroshima, Japan +1
Diagnosing and Repairing Citation Failures in Generative Engine Optimization
Mar 10, 2026Zhihua Tian, Yuhan Chen, Yao Tang +2Generative Engine Optimization Generative AI Evaluation
Virginia Tech · Zhejiang University
AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
Mar 2, 2026Jiaqi Yuan, Jialu Wang, Zihan Wang +3Generative Engine Optimization
School of Computer Science and Engineering, Beihang University, Beijing, China · Independent Contributor, CA, United States
Controlling Output Rankings in LLM-Based Search via Generative Engine Optimization
Feb 3, 2026Haibo Jin, Huimin Zeng, Yifeng Luo +1Generative Engine Optimization Large Language Model-Based Recommendation
School of Information Sciences, University of Illinois at Urbana-Champaign, IL, USA
Role-Augmented Intent-Driven Generative Search Engine Optimization
Aug 15, 2025Xiaolu Chen, Jie Bao, Haojie Wu +3Generative Engine Optimization
School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China.

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How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews

Riley Grossman, Songjiang Liu, Michael K. Chen, Mike Smith+2

New Jersey Institute of Technology Newark, NJ, USA · Nanyang Technological University Singapore, Singapore · Indiana University Bloomington Bloomington, IN, USA

Generative Engine Optimization Generative Retrieval IR Evaluation

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Generative AI is being increasingly integrated into web search for the convenience it provides users. In this work, we aim to understand how generative AI disrupts web search by retrieving and presenting the information and sources differently from traditional search engines. We introduce a public benchmark dataset of 11,500 user queries to support our study and future research of generative search. We compare the search results returned by Google's search engine, the accompanying AI Overview (AIO), and Gemini Flash 2.5 for each query. We have made several key findings. First, we find that for 51.5% of representative, real-user queries, AIOs are generated, and are displayed above the organic search results. Controversial questions frequently result in an AIO. Second, we show that the retrieved sources are substantially different for each search engine (<0.2 average Jaccard similarity). Traditional Google search is significantly more likely to retrieve information from popular or institutional websites in government or education, while generative search engines are significantly more likely to retrieve Google-owned content. Third, we observe that websites that block Google's AI crawler are significantly less likely to be retrieved by AIOs, despite having access to the content. Finally, AIOs are less consistent when processing two runs of the same query, and are less robust to minor query edits. Our findings have important implications for understanding how generative search impacts website visibility, the effectiveness of generative engine optimization techniques, and the information users receive. We call for revenue frameworks to foster a sustainable and mutually beneficial ecosystem for publishers and generative search providers.